rate, 0 ≤ r ≤ 1, such that the following quadratic inequality is satisfied,
[
]
{
0.5 1 (2 ) (1 ) ( )}
0.5 (2 ) 0 5 . 0 Dr2 +r D −τeH −eH + −eH 2h eH + yLB − DτeH −eH + yLA> where A≡[(1−eH)2(c+τ(1−c))eH(2−eH)−τ(1−eH)2ceH(2−eH)] and B≡[(1−eH)2(1−c)−eH(2−eH)],then securitization is feasible and optimal for any bank that would choose securitization were it able to commit to the policy of subsidization.
Obviously, other equilibria could exist. But, the point is that there can exist equilibria where the costs of bankruptcy are avoided by using off-balance sheet financing.
G. Summary and Empirical Implications
The conclusion of the above analysis is that the value of SPVs lies in their ability to minimize expected bankruptcy costs -- securitization arises to avoid bankruptcy costs. By financing the firm in pieces, control rights to the business decisions are separated from the financing decisions. The sponsor maintains control over the business while the financing is done via SPVs that are passive; that is, there are no control rights associated with the SPVs’ assets. Bankruptcy is a process of transferring control rights over corporate assets. Off-balance sheet financing reduces the amount of assets that are subject to this expensive and lengthy process.
We have argued that the ability to finance off-balance sheet via the debt of SPVs is critically dependent on a relational, or implicit, contract between the SPV sponsor and investors. The relational contract depends upon repeated use of off-balance sheet financing. We showed that this repetition can lead to an equilibrium with implicit recourse. Such an equilibrium implements the outcome of the equilibrium with formal commitments (Problem III), were such contracts possible. The comparative static properties of the Implicit Recourse Equilibrium are based on the result that the equilibrium outcomes of the Implicit Recourse Equilibrium are the same as the commitment equilibrium.
The idea of a relational contract supporting the feasibility of SPVs leads to our first set of empirical tests, namely, that the trigger strategy can only provide intertemporal incentives for the sponsor insofar as the sponsor exists. If the sponsor is so risky that there is a chance the sponsor will fail, and be unable to support the SPV, then investors will not purchase the SPV debt. We examine this idea by testing the hypothesis that investors, in pricing the debt of the SPV, care about the risk of the sponsor defaulting, above and beyond the risks of the SPV’s assets.
The second hypothesis that we empirically investigate is suggested by Corollary 1. Because the Implicit Recourse Equilibrium implements the outcome with formal commitment, Corollary 1 also describes the repeated equilibrium with implicit recourse. Corollary 1 says that the profitability of off-balance sheet financing is increasing in the bankruptcy cost, c, and increasing in the riskiness of the project (i.e., the chance of bankruptcy), (1–eH). In other words, riskier
sponsors should securitize more, ceteris paribus. Bankruptcy costs are not observable, but the riskiness of the firm can be proxied for by its firm bond rating.
V. Data
The rest of the paper empirically examines these two hypotheses. Our analysis suggests that the risk of a sponsoring firm should, because of implicit recourse, affect the risk of the ABS that are issued by its SPVs. We measure the sponsor’s risk by its bond rating, and focus on two ways that this risk might be manifested. As mentioned above, we first consider whether investors care about the strength of the sponsoring firm, above and beyond the characteristics of the ABS themselves. Second, we consider whether riskier firms are more likely to securitize in the first place. To these ends we utilize a number of datasets.
To investigate our first topic, investors’ sensitivity to the sponsor’s strength, we obtained from Moody’s a unique dataset describing every credit-card ABS issued between 1988:06 and 1999:05 that Moody’s tracked. This covers essentially all credit-card ABS through mid 1999. The dataset includes a detailed summary of the structure of each ABS, including the size and maturity of each ABS tranche. It summarizes the credit enhancements behind each tranche, such as the existence of any letters of credit, cash collateral accounts, and reserve accounts. Moody’s also calculated the amount of direct subordination behind each A and B tranche.24 These variables contain the information about the ABS structure that investors observed at the time of issuance. Further, the dataset includes some information about the asset collateral underlying each ABS, such as the age distribution of the credit-card accounts. Also included is the month-by-month ex post performance of each note, in particular the excess spread and its components like the chargeoff rate. The sample used below includes only the A and B tranches, i.e., the tranches that were sold publicly.
24
The amount of subordination behind the A note is calculated as (BalB+BalC)/(BalA+BalB+BalC), where BalX is the size (the balance) of tranche X when it exists. The dataset provided the current amount of subordination using current balances. For our analysis below, we want the original amount of subordination at the time of issuance. We were able to estimate this given the original balance sizes of the A and B notes, as well as an estimate of the size of any C note. The size of C notes is not directly publicly available, but we backed out their current size from the reported current amount of subordination behind the B notes. We used this to estimate the original amount of subordination behind the A and B notes.
Although it is difficult to find pricing information on credit-card ABS, we obtained from Lehman Brothers a dataset containing the initial yields on a large subset of these bonds that were issued in 1997-1999, for both the A and B notes. We obtained similar data from Asset Sales Reports for bonds that were issued before 1997. We computed the initial spread as the initial yield minus one month LIBOR at the time of issuance. We also collected Moody’s ratings from Bloomberg for the sponsors of each ABS in the Moody’s dataset above, which are typically banks. We use the bank’s senior unsecured bond rating at issuance.25
To investigate our second topic, an analysis of which banks securitize, we use the bank (“entity’) -level Call Report panel data that comes from the regulatory filings that banks file each quarter, from 1991:09 to 2000:06. Before 1996 we use only the third quarter (September) data, since credit card securitizations were reported only in the third quarter during that period. We also obtained from Moody’s a large dataset of all of their ratings of banks’ long-term senior obligations, including an ID variable that allowed us to match this data to the Call Report ID variables. Accordingly our sample includes all the banks in the Call Report dataset for which we have a matching rating.26 This yields a sample of almost 400 banks and over 5000 bank-quarters, which is large relative to the samples analyzed in previous related literature.
VI. Empirical Tests: Are there Implicit Recourse Commitments?
In this section we analyze the determinants of the spread on the notes issued by the SPVs to the capital markets. Borgman and Flannery (1997) also analyze asset-backed security spreads, over the period 1990-1995. They find that credit card ABS require a lower market spread if the sponsoring firm is a bank or if the sponsor includes guarantees as a form of credit enhancement.
The unit of observation is a transaction, that is a note issuance, either the A note or the B note. We examine the cross sectional determinants of the spreads. The spreads provide us with investors’ assessment of the risk factors behind each note. All the A notes were on issuance rated AAA by Moody’s.27 If these ratings are sufficient statistics for default, then the probability of
25
We use the rating of the current owner of the ABS trust, accounting for any mergers and acquisitions.
26
Since small banks are less likely to be rated, matches are most common for the larger banks.
27
All but two of the B notes were initially rated A; the two exceptions were rated AA. By distinguishing the A- and B-notes, the analysis implicitly controls for any clientele effects.
default should be the same for all the A notes and in the simplest case (e.g., if there is no implicit recourse) presumably investors would pay the same initial price for them. Even if there are differences across notes in the quality of the underlying assets or in other factors, the securitizations should be structured to offset these differences and yield the same probability of default. As discussed above, to test for the existence of a relational contract allowing for recourse, we examine whether other factors affect the initial prices of the notes, in particular whether the strength of the sponsor matters, as estimated by its senior unsecured credit rating at the time of issuance. Specifically, we estimate equations of the following form:
Spreadi,j,k,t = β0’Timet + β1’Structurei + β2’Assetsi + β3’Trustj + β4’Ratingk,t + εi,j,k,t, (4)
where Spreadi,j,k,t is the initial spread (net of one month LIBOR) on note i from trust j and sponsor
k at the time t of issuance. Time is a vector of year dummies that control for time varying risk
premia as well as all other macroeconomic factors, including the tremendous growth in the ABS market over the sample period. Structurei represents the structure of tranche i at the time of issuance, such as the degree of subordination and other credit enhancements supporting it, and
Assetsi represents the quality of the credit-card assets underlying the tranche at that time. Trustj